Jakob Ruess

The use of continuous-time Markov chains for modeling biochemical reaction networks in which the inherent randomness of the molecular interactions cannot be ignored is nowadays common practice. Methods for parameter inference and experimental design for such models, however, still remain in their infancy. The main limitation is that such methods usually require iteratively solving the chemical master equation, a task which is only possible for the simplest of systems.
In this talk I will propose methods for parameter inference and experimental design which are based on only low-order moments instead of the entire probability distribution of the system. These moments can often still be computed (or at least approximated) in cases where solving the chemical master equation is impossible. Consequently, the proposed methods are applicable for many biological systems which are too complicated for the previously existing parameter inference and experimental design methods. I will demonstrate this by studying two systems in yeast: the transcriptional activation induced by osmotic stress and an engineered light-switch gene expression system.